Recommender System with Machine Learning and Artificial Intelligence

1An Introduction to Basic Concepts on Recommender Systems

Pooja Rana, Nishi Jain and Usha Mittal*

Department of Computer Science and Engineering, Lovely Professional University, Phagwara, India

Abstract

In today’s world, we find a wide range of possibilities of any search that we do online and we might find difficulties in choosing what we actually need. To address these issues, recommendation System plays a major role. A recommender system is a filtering system that filters the data using different algorithms and recommends the most relevant data to the user. For instance, a recommender system for e-commerce requires a past history of the site and if the user is not having any past history then the recommender system recommends the bestselling product or most popular product present in the market. Recommendation systems are effective tools for personalization, are always up-to-date, and gives a recommendation based on actual user behavior. Besides being useful in buying products it has a few disadvantages like it is difficult to set up and get running as they are database-driven. Sometimes recommendations are wrong which makes customers unsatisfied. Recommender system is used in different areas like recommendation for entertainment such as movies, songs etc., e-learning web site recommendation, newspaper recommendation and e-mail filters.

In this chapter, various recommendation techniques with their pros and cons and different evaluation metrices has been discussed.

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